Papers by Anirudh Phukan
Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs (2025.naacl-long)
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Anirudh Phukan, Divyansh Divyansh, Harshit Kumar Morj, Vaishnavi Vaishnavi, Apoorv Saxena, Koustava Goswami
| Challenge: | Large Multimodal Models are plagued by hallucinations that limit their reliability and adoption. |
| Approach: | They propose a method that leverages contextual token embeddings from LMMs to detect hallucinations. |
| Outcome: | The proposed method improves hallucination detection and grounding across diverse categories while excelling in tasks requiring contextual understanding. |
Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used for question answering . lack of explicit references or attributions hinders ability to verify accuracy of answers . |
| Approach: | They propose a method for attribution in contextual question answering . they use hidden state representations of large language models to identify copied segments . |
| Outcome: | The proposed method performs better than GPT-4 at identifying verbatim copied segments in LLM generations and attributing these segments to their source. |
PLD+: Accelerating LLM Inference by Leveraging Language Model Artifacts (2025.findings-naacl)
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| Challenge: | speculative decoding is a novel decoding paradigm for large language models . however, its use is limited by its computational resources and fine-tuning requirements . |
| Approach: | They propose a tuning-free approach that accelerates inference of large language models . they use draft and verify principle to accelerate inference process . |
| Outcome: | The proposed approach outperforms tuning-free approaches on input-guided tasks and outperformed state-of-the-art EAGLE on four of the tasks. |
Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG (2026.findings-acl)
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| Challenge: | Query enhancement techniques are now standard in retrieval-augmented generation systems, yet their impact on these biases remains unexplored. |
| Approach: | They evaluate query enhancement techniques that improve retrieval quality . they find that simple rewriting reduces bias through increased score variance . no technique uniformly addresses all biases, and effects vary substantially across retrievers . |
| Outcome: | The proposed method achieves strongest aggregate reduction, but fails under adversarial conditions where multiple biases combine. |